The objective of this R01 application is to develop a rapid method for imaging regional ventilation and lung compliance in small animals without contrast agents. Much of our current understanding of the normal functioning of the lung and mechanisms of lung disease comes from small animal studies. However, lung function imaging in small animal models is technically challenging due to motion and the relatively small size of the lungs. Pulmonary function testing using plethysmography has been employed to assess lung function and injury with limited validity and utility, particularly in small animals. Additionally, only aggregate measures of functional performance are produced and no regional lung changes can be assessed. An improved imaging method that could provide spatially- and temporally-resolved information regarding ventilation would be of great value to those studying basic pulmonary physiology and the onset and progression of a large range of respiratory diseases. It would also facilitate drug discovery and efficacy studies aimed to mitigate respiratory pathology. The ideal method would provide quantitative regional functional information, be applicable to longitudinal studies (low radiation dose), and have a simple and affordable implementation that permits widespread use. Currently available imaging methods including micro-CT or MRI fall short in one or more of these requirements. To address this need, we will establish and evaluate a novel, easy to implement, and highly effective X- ray phase-contrast (XPC) method for ventilation imaging in small animal models. The lung is ideally suited to XPC imaging because it is comprised mainly of air spaces separated by thin tissue structures. The air-tissue interfaces cause the X-ray beam to experience numerous and strong refractions that produce a distinctive texture in the intensity measured over the lungs known as speckle. Detailed information regarding the regional lung air volume (RLAV) distribution is encoded in the speckle. The benefits of exploiting lung speckle for detecting and monitoring lung function are numerous but remain entirely unexplored for benchtop imaging. Our approach involves a high degree of technical innovation regarding image formation methods and will significantly extend the current boundaries of functional lung imaging in small animals. The proposed method, referred to as parametric XPC (P-XPC) imaging, will produce 2D parametric images that depict the projected RLAV distribution. When differential images are computed for any given two points in the breathing cycle, ventilation or lung compliance imaging will be achieved. Preliminary in vivo and computational studies have been conducted in support of the proposed research. The specific aims of the project are as follows. Aim 1: Develop P-XPC image formation methods for estimating the projected RLAV distribution; Aim 2: Optimize an XPC imaging system for P-XPC imaging. Aim 3: Evaluate the diagnostic capability of P-XPC imaging in two pre-clinical animal models of disease in vivo.